An AI dataset carves new paths to tornado detection
TorNet, a public artificial intelligence dataset, could help models reveal when and why tornadoes form, improving forecasters' ability to issue warnings.
TorNet, a public artificial intelligence dataset, could help models reveal when and why tornadoes form, improving forecasters' ability to issue warnings.
A new technique can be used to predict the actions of human or AI agents who behave suboptimally while working toward unknown goals.
Lincoln Laboratory researchers are using AI to get a better picture of the atmospheric layer closest to Earth's surface. Their techniques could improve weather and drought prediction.
MIT Sea Grant students apply machine learning to support local aquaculture hatcheries.
MIT Center for Transportation and Logistics Director Matthias Winkenbach uses AI to make vehicle routing more efficient and adaptable for unexpected events.
A CSAIL study highlights why it is so challenging to program a quantum computer to run a quantum algorithm, and offers a conceptual model for a more user-friendly quantum computer.
Graduate student Hammaad Adam is working to increase the supply of organs available for transplants, saving lives and improving health equity.
Researchers create a curious machine-learning model that finds a wider variety of prompts for training a chatbot to avoid hateful or harmful output.
The 16 finalists — representing every school at MIT — will explore generative AI’s impact on privacy, art, drug discovery, aging, and more.
The new approach “nudges” existing climate simulations closer to future reality.
With help from a large language model, MIT engineers enabled robots to self-correct after missteps and carry on with their chores.
Researchers demonstrate a technique that can be used to probe a model to see what it knows about new subjects.
Novel method makes tools like Stable Diffusion and DALL-E-3 faster by simplifying the image-generating process to a single step while maintaining or enhancing image quality.
FeatUp, developed by MIT CSAIL researchers, boosts the resolution of any deep network or visual foundation for computer vision systems.
At the MIT Quantum Hackathon, a community tackles quantum computing challenges.